机器人任务规划：基于PDDL和ASP的任务规划系统实验比较研究

姜雨倩; 张世琦; Piyush KHANDELWAL; Peter STONE

doi:10.1631/FITEE.1800514

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机器人任务规划：基于PDDL和ASP的任务规划系统实验比较研究

智能机器人专题 | Updated：2022-05-19

- 机器人任务规划：基于PDDL和ASP的任务规划系统实验比较研究
- Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
- 信息与电子工程前沿（英文） 2019年20卷第3期页码：363-373
- Affiliations：
  
  Department of Computer Science, The University of Texas at Austin, TX 78712, USA
  Department of Computer Science, The State University of New York at Binghamton, NY 13902, USA
  Cogitai, Inc., TX 78756, USA
- Author bio：
  
  [ "Yu-qian JIANG, E-mail: jiangyuqian@utexas.edu" ]
  Shi-qi ZHANG, E-mail: szhang@cs.binghamton.edu
  [ "Piyush KHANDELWAL, E-mail: piyushk@gmail.com" ]
  [ "Peter STONE, E-mail: pstone@cs.utexas.edu" ]
- Funds：
- DOI：10.1631/FITEE.1800514
  中图分类号： TP242
- 收稿：2018-08-29，
  
  修回：2019-;3-14，
  
  纸质出版：2019-03
- Accepted：
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姜雨倩, 张世琦, Piyush KHANDELWAL, 等. 机器人任务规划：基于PDDL和ASP的任务规划系统实验比较研究[J]. 信息与电子工程前沿（英文）, 2019,20(3):363-373.

Yu-qian JIANG, Shi-qi ZHANG, Piyush KHANDELWAL, et al. Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 363-373.
姜雨倩, 张世琦, Piyush KHANDELWAL, 等. 机器人任务规划：基于PDDL和ASP的任务规划系统实验比较研究[J]. 信息与电子工程前沿（英文）, 2019,20(3):363-373. DOI： 10.1631/FITEE.1800514.

Yu-qian JIANG, Shi-qi ZHANG, Piyush KHANDELWAL, et al. Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 363-373. DOI： 10.1631/FITEE.1800514.

摘要

面对无法用单一动作解决的复杂任务时，机器人需要通过任务规划算法生成一个动作序列。现有任务规划器可以帮助智能机器人开发人员解决许多种类任务规划问题。然而，不同规划器有不同优势和劣势，没有统一准则针对问题选择规划器。本文比较了目前最先进的基于规划领域定义语言（planning domain description language

PDDL）和回答集程序（answer set programming

ASP）规划器的性能。PDDL是特别为任务规划而设计的动作语言，被广泛应用于各种规划问题。ASP主要用于知识推理，同时也能解决任务规划问题。针对这两种语言，本文使用尽可能相同的领域描述。实验结果表明，基于PDDL的规划器善于解决需要生成较多待执行动作的问题，而基于ASP的规划器更适合解决涉及对象较多的任务，或需对前提条件和后置条件做复杂推理的规划问题。针对具体机器人规划问题，本文得到的结论可以帮助研究人员从通用规划系统中选择合适规划器。

Abstract

Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However

many different planners exist

each with different strengths and weaknesses

and there are no general rules for which planner would be best to apply to a given problem. In this study

we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning

and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning

but can also be used to solve task planning problems. Given domain encodings that are as similar as possible

we find that PDDL-based planners perform better on problems with longer solutions

and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.

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